scintillator detector (PSD). The sensitive volume of the PSD is approxi- mately 1 mm long by 3 mm diameter. The phantom was treated with ten different plans, which are copies of clinical treatments of patients with implanted cardiac device. The phantom was placed on the treatment couch with the treatment isocenter positioned in the same way as for clinical patients. The PSD was positioned at the same distance from the isocenter as the center of the cardiac device of the patient and at the same depth. Of all 10 radiation treatments, two were for head-and-neck, five for lung and three for breast. Three cases (2 head-and-neck and 1 lung), were treated with Intensity-Modulated Radiation Therapy (IMRT) and the others were standard, 3D conformal radiation treatments (3DCRT). Results: The mean cardiac devices dose reduction was 23% for all measured treatments. Dose reduction was most important for breast cases with a mean reduction of 43%. The smallest dose reduction was for lung cases. In one case (lung IMRT), the lead shield induced a small, clinically insignificant increase of dose over the full treatment. In that case, four out of six fields were posterior. The lead shield reduces the dose for anterior fields and slightly increases the dose for posterior fields. It was also observed that the reduction of dose is more important for 23 MV beams, for breast cancer, the reduction of dose for 23 MV field is seven times more important than the reduction measured for the same field at 6 MV. The treatment information and results are shown in table for all cases except one patient. Conclusions: It was demonstrated that a lead shield can be efficiently used for reducing doses to cardiac devices for patient treated in radiation therapy. A lead shielding provided a mean dose reduction of 23% for different treatment area. When the cardiac devices is less than 20 cm from the treatment fields, placing a lead shielding is a useful and simple method for achieving significant dose reduction to radiation-sensitive cardiac devices. Author Disclosure: A. Bourgouin: None. N. Varfalvy: None. L. Beaulieu: None. L. Archambault: None. 3259 Applying Human Factors Analysis and Classification System (HFACS) to the Analysis of Good Catches in Radiation Oncology P. Mosaly, L.M. Mazur, S. Miller, M. Eblan, andE.L. Jones; University of North Carolina, Chapel Hill, NC Purpose/Objective(s): To apply human factors analysis and classification system (HFACS) to the analysis of the good catches (near miss) in radi- ation oncology. Materials/Methods: HFACS has been successfully applied to analyze errors and good catches in various domains, including aviation, trans- portation, and nuclear power plants. Twenty-one good catches were selectively chosen from the department database to perform the analysis using HFACS. Two resident physicians volunteered to participate in the study and were familiarized with HFACS through 1 hour training. The participants performed the analysis independently, and as a team to reach consensus. Next, the participants met with two HFACS experts to compare their analysis results. An inter-rater reliability analysis using Cohen Kappa (k) statistics was performed to determine consistency (i.e., strong [>0.8], moderate [0.60> k 0.80], and weak [0.60]) among (i) the two partic- ipants, (ii) participants and experts. Results: The inter-rater reliability analysis showed (Table) (i) the partic- ipants agreed on classification 96% of the times, indicating ‘strong agreement’ in classifying good catches into the five levels of HFACS (k Z 0.95, p < 0.001); (ii) the participants were in ‘moderate agreement’ with experts (69%, k Z 0.63, p Z 0.07), and in ‘moderate’ to ‘strong agree- ment’ for individual layers of HFACS. Conclusions: The current study results indicate moderate to strong inter- rater reliability between participants/experts in classifying good catches into the five levels of HFACS. This suggests that HFACS can be successfully applied for the analysis of good catches in radiation oncology. However, moderate inter-rater reliability between participants/experts in classifying good catches into the HFACS sub-categories, especially the ‘unsafe act’ layer, suggest a need for a more ‘formal’ education and training on HFACS if higher levels of classification details are expected. We recognize that there are inherent limitations to this study (e.g., small samples of good catches with unequal representation of all categories and layers of HFACS model, small number of participants, training, etc.). Nevertheless, this pilot study seems reasonable and the data provide some quantification for future design of analysis and classification of good catches and errors in radiation oncology; and demonstrate strategies for incorporation of safety concepts in HFACS for resident education. Author Disclosure: P. Mosaly: None. L.M. Mazur: None. S. Miller: None. M. Eblan: None. E.L. Jones: None. 3260 Bite Blocks Can Reduce Setup Uncertainty Related to Weight Loss During Head and Neck IMRT: Preliminary Results of a 3-Modality Image Guidance Protocol D. Asher, 1 J.D. Evans, 1 M.E. Schutzer, 1 N. Dogan, 2 M. Fatyga, 3 F. Sleeman, 1 and S. Song 1 ; 1 Virginia Commonwealth University, Richmond, VA, 2 University of Miami, Miami, FL, 3 Mayo Clinic, Scottsdale, AZ Purpose/Objective(s): Adaptive radiation therapy (ART) is of substantial interest in head and neck (H&N) IMRT as changes in weight, tumor, and normal tissue can compromise the accuracy of the original treatment plan. The aim of this study is to investigate the daily setup uncertainty over the course of treatment using multiple image techniques and multiple factors that alter patient setup integrity. Materials/Methods: Thirteen patients with biopsy proven H&N tumors were enrolled under an IRB approved imaging protocol. Each patient was treated to 70 Gy in 33-35 fractions with concurrent chemotherapy. Pre- treatment imaging with cone beam CT, kV orthogonal on-board imaging, and patient motion-tracking system was performed daily and fan beam CT imaging was performed weekly. Preliminary data focuses on the patient motion-tracking system, which is the imaging modality used to align patients for H&N IMRT in our clinic. In this preliminary data analysis of the first 5 successfully enrolled patients, the daily shifts are grouped by treatment week to observe trends over time. The daily pre-treatment shift magnitudes were averaged over each week to quantify the mean shift distance. The standard deviation of the daily shifts was calculated for each week to quantify the random setup uncertainty. Poster Viewing Abstract 3259; Table Percentage cases agreed between 2 groups for the root cause analysis of good catches using HFACS HFACS layers Between participants Between participants and experts % Agreed Kappa(k) (P value) % Agreed Kappa(k) (P value) Overall 85 0.82 (P < .001) 69 0.63 (P Z .07) Unsafe act 1 90 0.86 66 0.61 Precondition for unsafe act 2 94 0.90 88 0.78 Unsafe supervision 3 100 1.00 100 1.00 Organizational influence 4 100 1.00 80 0.75 Outside factors 5 100 1.00 100 1.00 HFACS description of subcategories for each layer: 1 Unsafe act e Unintended (attention failure, memory failure, mistake) and intended (routine or exceptional). 2 Precondition for unsafe act e Environmental (technical or phys- ical), condition of the operator (adverse mental or physiological state), and physical/mental limitations (personal factors, coordination/ communication, or fitness for duty). 3 Unsafe supervision e Inadequate supervision, planned inappro- priate operations, failed to correct a known problem, or supervisory violation. 4 Organizational influence e Organization culture, organization policies, or human resource management. 5 Outside factors e Regulatory factors. Volume 87  Number 2S  Supplement 2013 Poster Viewing Abstracts S683